Function cellInfo() can be used to query information of a single cell
from a sdcProblem object. If the instance has already been protected
using protectTable(), the information is retrieved from the final protected
dataset, otherwise from the current state of the instance.
cell_info(object, specs, ...)an object of class sdcProblem
input that defines which cells to query; the function expects either (see examples below)
a named character vector: with names referring to the names of the dimensional variables and the values to its labels. In this case each vector-element must contain a single value (label)
a data.frame where the column-names refer to the names of the dimensional
variables and the values to the labels
additional parameters for potential future use, currently unused.
a data.frame with a row for each of the queried cells; the object
contains the following columns:
id: numeric vector of length 1 specifying the numerical index of the cell
a column strID if object has not yet been protected
one column for each dimensional variable
a column freq containing the cell-frequencies
if available, one column for each (possible) numerical value that was tabulated
a column sdcStatus with the current sdc code
is_primsupp: is TRUE if the cell is a primary sensitive cell
is_secondsupp: is TRUE if the cell is a secondary suppressed cell
# as in makeProblem() with a single primary suppression
p <- sdc_testproblem(with_supps = TRUE)
sdcProb2df(p)
#> Key: <strID>
#> strID freq sdcStatus region gender region_o gender_o
#> <char> <num> <char> <char> <char> <char> <char>
#> 1: 0000 100 s 00 00 total total
#> 2: 0001 55 s 00 01 total male
#> 3: 0002 45 s 00 02 total female
#> 4: 0100 20 s 01 00 A total
#> 5: 0101 18 s 01 01 A male
#> 6: 0102 2 u 01 02 A female
#> 7: 0200 33 s 02 00 B total
#> 8: 0201 14 s 02 01 B male
#> 9: 0202 19 s 02 02 B female
#> 10: 0300 22 s 03 00 C total
#> 11: 0301 12 s 03 01 C male
#> 12: 0302 10 s 03 02 C female
#> 13: 0400 25 s 04 00 D total
#> 14: 0401 11 s 04 01 D male
#> 15: 0402 14 s 04 02 D female
# vector input
specs_vec <- c(region = "D", gender = "male")
cell_info(p, specs = specs_vec)
#> Key: <strID>
#> id strID region gender freq val sdcStatus is_primsupp is_secondsupp
#> <int> <char> <char> <char> <num> <num> <char> <lgcl> <lgcl>
#> 1: 14 0401 D male 11 366 s FALSE FALSE
# data.frame input
specs_df <- data.frame(
region = c("A", "D", "A"),
gender = c("male", "female", "female")
)
cell_info(p, specs = specs_df)
#> id strID region gender freq val sdcStatus is_primsupp is_secondsupp
#> <int> <char> <char> <char> <num> <num> <char> <lgcl> <lgcl>
#> 1: 5 0101 A male 18 178 s FALSE FALSE
#> 2: 15 0402 D female 14 152 s FALSE FALSE
#> 3: 6 0102 A female 2 20 u TRUE FALSE
# protect the table
p_safe <- protectTable(p, method = "SIMPLEHEURISTIC")
# re-apply
cell_info(p_safe, specs = specs_df)
#> id strID region gender freq val sdcStatus is_primsupp is_secondsupp
#> <int> <char> <char> <char> <num> <num> <char> <lgcl> <lgcl>
#> 1: 5 0101 A male 18 178 x FALSE TRUE
#> 2: 15 0402 D female 14 152 s FALSE FALSE
#> 3: 6 0102 A female 2 20 u TRUE FALSE